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Could you guide us through the Naive Bayes algorithm and the basis for setting its classification threshold?

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Answers

Anonymous

4Strong
Naive Bayes computes the probability of the class given the features:
P(class | features) = P(features | class) * P(class) / P(features).
Navie Bayes assumes that all the features are independent given the class so that the likelihood can be computed easily as a product of all the individual likelihood of features. For each class, it computes a product of all individual features given the class and the prior probability of that class.
The class with the highest probability is chosen as the resulting class.
For a binary classification problem, the default threshold is 0.5, but it can be tuned manually (eg. 0.7) to balance precision and recall given the problem's requirement.  For a multi-class problem, the default threshold is 1 / number of classes, and we can use the one-vs-rest technique.

Anonymous

3.3Strong
In Naive bayes, the probablity of a sample to a class is calculated as
P(class | sample) = P(sample | class) P(class) / P(sample)
It assumes the samples are independent. In general, the threshold is kept in 0.5 insuring a balance between detection of classes. For example, in case of Ham/Spam classifier, 0.5 allows for a balance between TP and FP of both classes.
However, if we would like to detect as many as Spam and keen to accpt some Ham may be classified as Spam, then we can play with threshold
  • Could you guide us through the Naive Bayes algorithm and the basis for setting its classification threshold?
  • Please describe the fundamental concepts of Naive Bayes and your criteria for threshold determination.,
  • Can you outline the core principles of Naive Bayes and how you establish a classification cut-off?
  • How would you explain the basics of the Naive Bayes algorithm, including your method for threshold selection?
  • In what way do you understand the principles of Naive Bayes and determine the classification threshold?
  • I'd like to hear about the Naive Bayes algorithm and your approach to threshold determination. Can you discuss this?
  • What are the essential elements of Naive Bayes and how do you decide on the classification threshold?
  • Can you explain the foundational aspects of the Naive Bayes algorithm and how you set the classification boundary?
  • Please expound on the Naive Bayes algorithm and your process for deciding the classification threshold.,
  • How do you approach explaining the Naive Bayes algorithm and setting the classification threshold?
  • Walk us through the basic principles of Naive Bayes algorithm. How do you determine the threshold for classification in your Naive Bayes model?
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